[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121081-en":3,"doc-seo-121081-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121081,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine Learning Robustness: A Primer - Chapter 1 - Introduction","This chapter establishes robustness as a core foundation for building trustworthy artificial intelligence systems in machine learning. It defines ML model robustness as the ability to keep stable, reliable predictive performance under diverse and unexpected input conditions, and frames it against generalizability. The discussion distinguishes adversarial and non-adversarial robustness, outlines quantitative metrics and assurance indicators such as reproducibility and explainability, and analyzes key obstacles like data bias, excessive model complexity, and underspecified ML pipelines. It surveys assessment and improvement techniques spanning attacks, data shifts, deep learning testing, and mitigation strategies from debiasing and augmentation to training, smoothing, ensembling, pruning, and model repairs, while highlighting ongoing estimation challenges.","arXiv :2404 .00897v3 [ cs .LG] 4 May 2024  \nChapter 1  \nMachine Learning Robustness: A Primer  \nHoussem Ben Braiek and Foutse Khomh {houssem.ben-braiek, [foutse.khomh}@polymtl.ca](foutse.khomh}@polymtl.ca)  \nABSTRACT  \nThis chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions. ML robustness is dissected through several lenses: its complementarity with generalizability; its status as a requirement for trustworthy AI; its adversarial vs non-adversarial aspects; its quantitative metrics; and its indicators such as reproducibility and explainability. The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines. It surveys key techniques for robustness assessment from a broad perspective, including adversarial attacks, encompassing both digital and physical realms. It covers non-adversarial data shifts and nuances of Deep Learning (DL) software testing methodologies. The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation. Further examination includes a variety of model-centric methods such as transfer learning, adversarial training, and randomized smoothing. Lastly, post-training methods are discussed, including ensemble techniques, pruning, and model repairs, emerging as cost-effective strategies to make models more resilient against the unpredictable. This chapter underscores the ongoing challenges and limitations in estimating and achieving ML robustness by existing approaches. It offers insights and directions for future research on this crucial concept, as a prerequisite for trustworthy AI systems.  \nKEYWORDS  \nMachine Learning, Deep Learning, Robust AI, Trustworthy AI, Adversarial Robustness, Non-Adversarial Robustness, Model Verification, DL Software Testing, Robust Training, Robustness Assurance  \n1.1 DEFINITION  \nIn general, robustness is a predicate that applies to a single entity. For instance, we might consider a sensor robust if it is resilient to disturbances from the environment. In more detail, robustness refers to the ability of a system, model,  \n2  \nor entity to maintain stable and reliable performance across a broad spectrum of conditions, variations, or challenges, demonstrating resilience and adaptability in the face of uncertainties or unexpected changes. Hence, we define below the Machine Learning (ML) Model robustness based on the general definition of robustness in ML outlined by Freiesleben and Grote in [1] .  \nML Model robustness denotes the capacity of a model to sustain stable predictive performance in the face of variations and changes in the input data.  \nAccording to this definition, ML Model robustness is an epistemic concept that presupposes the generalizability of the model’s inductive bias on the in-distribution data, and extends further to evaluate the model’s stability and resilience in real-world deployment scenarios. The following Table presents concrete illustrations of how performance degradation and data changes manifest in real-world scenarios.  \n\n| Examples of variations and changes in the input data: |\n| --- |\n| – Variations in input features or object recognition patterns that challenge the inductive bias learned by the model from the training data.\u003Cbr>– Production data distribution shifts due to naturally occurring distortions, such as lighting conditions or other environmental factors.\u003Cbr>– Malicious input alterations that are deliberately introduced by an attacker to fool the model or even steer its prediction in a desired direction.\u003Cbr>– Gradual data drift resulting from external factors, such as e","cbCaihsdg2qh7VwC","https://ap.wps.com/l/cbCaihsdg2qh7VwC","pdf",424370,1,43,"English","en",105,"# Chapter 1 Machine Learning Robustness: A Primer\n## Definition\n## Variations in Input Data and Threats to Stable Performance\n## Tolerance Levels and Deployment Considerations","[{\"question\":\"How is robustness defined for machine learning models in this chapter?\",\"answer\":\"ML model robustness is defined as the capacity to sustain stable predictive performance when input data varies and changes. It extends beyond in-distribution behavior to stability and resilience during real-world deployment.\"},{\"question\":\"What kinds of input changes and threats are used to illustrate robustness challenges?\",\"answer\":\"The chapter lists variations in input features and recognition patterns, production data distribution shifts from environmental distortions, malicious alterations such as adversarial inputs and data poisoning, and gradual data drift causing concept drift. It also includes issues like spurious correlations, underrepresented edge cases, and inability to generalize under drifted data.\"},{\"question\":\"Why is specifying a tolerance level important when assessing robustness?\",\"answer\":\"Robustness assessment is difficult if the range of input changes and the performance goal are left unspecified. The model may only need to maintain performance up to an application-dependent tolerance, which can differ widely between domains such as clinical decision support and spam detection.\"}]","Machine Learning Robustness: A Primer - Chapter 1 - Introduction | PDF",1785733621,108,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-robustness-a-primer-chapter-1-introduction","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-robustness-a-primer-chapter-1-introduction/121081/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How is robustness defined for machine learning models in this chapter?","Question",{"text":75,"@type":76},"ML model robustness is defined as the capacity to sustain stable predictive performance when input data varies and changes. It extends beyond in-distribution behavior to stability and resilience during real-world deployment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What kinds of input changes and threats are used to illustrate robustness challenges?",{"text":80,"@type":76},"The chapter lists variations in input features and recognition patterns, production data distribution shifts from environmental distortions, malicious alterations such as adversarial inputs and data poisoning, and gradual data drift causing concept drift. It also includes issues like spurious correlations, underrepresented edge cases, and inability to generalize under drifted data.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is specifying a tolerance level important when assessing robustness?",{"text":84,"@type":76},"Robustness assessment is difficult if the range of input changes and the performance goal are left unspecified. 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